Mariano Salvetti | Universidad Nacional de Rosario (UNR) Argentina (original) (raw)

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Papers by Mariano Salvetti

In this paper, a new indexing and similarity search method based on dynamic selection of pivots i... more In this paper, a new indexing and similarity search method based on dynamic selection of pivots is presented. It uses Sparse Spatial Selection (SSS) for the initial selection of pivots. Two new selection policies of pivots are added, in order to the index suits itself to searches when it adapts to the metric space. The proposed structure automatically adjusts to the region where most of searches are made. In this way, the amount of distance computations during searches is reduced. The adjustment is done using the policy of 'the most candidate' for the incoming pivot selection, and the policy of 'the least discriminating' for the outgoing pivot selection.

Most search methods in metric spaces assume that the topology of the object collection is reasona... more Most search methods in metric spaces assume that the topology of the object collection is reasonably regular. However, there exist nested metric spaces, where objects in the collection can be grouped into clusters or subspaces, in such a way that different dimensions or variables explain the differences between objects inside each subspace. This paper proposes a two levels index to solve search problems in spaces with this topology. The idea is to have a first level with a list of clusters, which are identified and sorted using Sparse Spatial Selection (SSS) and Lists of Clusters techniques, and a second level having an index for each dense cluster, based on pivot selection, using SSS. It is also proposed for future work to adjust the second level indexes through dynamic pivots selection to adapt the pivots according to the searches performed in the database.

This paper presents a data structure based on Sparse Spatial Selection (SSS) for similarity searc... more This paper presents a data structure based on Sparse Spatial Selection (SSS) for similarity searching. An algorithm that tries periodically to adjust pivots to the use of database index is presented. This index is dynamic. In this way, it is possible to improve the amount of discriminations done by the pivots. So, the primary objective of indexes is achieved: to reduce the number of distance function evaluations, as it is showed in the experimentation.

Journal of Information and …, 2011

In this paper, a new indexing and similarity search method based on dynamic selection of pivots i... more In this paper, a new indexing and similarity search method based on dynamic selection of pivots is presented. It uses Sparse Spatial Selection (SSS) for the initial selection of pivots. Two new selection policies of pivots are added, in order to the index ...

Brazilian Symposium on Databases, 2010

In this paper, a new indexing and similarity search method based on dynamic selection of pivots i... more In this paper, a new indexing and similarity search method based on dynamic selection of pivots is presented. It uses Sparse Spatial Selection (SSS) for the initial selection of pivots. Two new selection policies of pivots are added, in order to the index suits itself to searches when it adapts to the metric space. The proposed structure automatically adjusts to the region where most of searches are made. In this way, the amount of distance computations during searches is reduced. The adjustment is done using the policy of 'the most candidate' for the incoming pivot selection, and the policy of 'the least discriminating' for the outgoing pivot selection.

Abstract In this paper, a new indexing and similarity search method based on dynamic selection of... more Abstract In this paper, a new indexing and similarity search method based on dynamic selection of pivots is presented. It uses Sparse Spatial Selection (SSS) for the initial selection of pivots. Two new selection policies of pivots are added, in order to the index ...

Journal of Information and …, 2011

In this paper, a new indexing and similarity search method based on dynamic selection of pivots i... more In this paper, a new indexing and similarity search method based on dynamic selection of pivots is presented. It uses Sparse Spatial Selection (SSS) for the initial selection of pivots. Two new selection policies of pivots are added, in order to the index ...

This paper presents a data structure based on Sparse Spatial Selection (SSS) for similarity searc... more This paper presents a data structure based on Sparse Spatial Selection (SSS) for similarity searching. An algorithm that tries periodically to adjust pivots to the use of database index is presented. This index is dynamic. In this way, it is possible to improve the amount of discriminations done by the pivots. So, the primary objective of indexes is achieved: to reduce the number of distance function evaluations, as it is showed in the experimentation.

In this paper, a new indexing and similarity search method based on dynamic selection of pivots i... more In this paper, a new indexing and similarity search method based on dynamic selection of pivots is presented. It uses Sparse Spatial Selection (SSS) for the initial selection of pivots. Two new selection policies of pivots are added, in order to the index suits itself to searches when it adapts to the metric space. The proposed structure automatically adjusts to the region where most of searches are made. In this way, the amount of distance computations during searches is reduced. The adjustment is done using the policy of 'the most candidate' for the incoming pivot selection, and the policy of 'the least discriminating' for the outgoing pivot selection.

Most search methods in metric spaces assume that the topology of the object collection is reasona... more Most search methods in metric spaces assume that the topology of the object collection is reasonably regular. However, there exist nested metric spaces, where objects in the collection can be grouped into clusters or subspaces, in such a way that different dimensions or variables explain the differences between objects inside each subspace. This paper proposes a two levels index to solve search problems in spaces with this topology. The idea is to have a first level with a list of clusters, which are identified and sorted using Sparse Spatial Selection (SSS) and Lists of Clusters techniques, and a second level having an index for each dense cluster, based on pivot selection, using SSS. It is also proposed for future work to adjust the second level indexes through dynamic pivots selection to adapt the pivots according to the searches performed in the database.

This paper presents a data structure based on Sparse Spatial Selection (SSS) for similarity searc... more This paper presents a data structure based on Sparse Spatial Selection (SSS) for similarity searching. An algorithm that tries periodically to adjust pivots to the use of database index is presented. This index is dynamic. In this way, it is possible to improve the amount of discriminations done by the pivots. So, the primary objective of indexes is achieved: to reduce the number of distance function evaluations, as it is showed in the experimentation.

Journal of Information and …, 2011

In this paper, a new indexing and similarity search method based on dynamic selection of pivots i... more In this paper, a new indexing and similarity search method based on dynamic selection of pivots is presented. It uses Sparse Spatial Selection (SSS) for the initial selection of pivots. Two new selection policies of pivots are added, in order to the index ...

Brazilian Symposium on Databases, 2010

In this paper, a new indexing and similarity search method based on dynamic selection of pivots i... more In this paper, a new indexing and similarity search method based on dynamic selection of pivots is presented. It uses Sparse Spatial Selection (SSS) for the initial selection of pivots. Two new selection policies of pivots are added, in order to the index suits itself to searches when it adapts to the metric space. The proposed structure automatically adjusts to the region where most of searches are made. In this way, the amount of distance computations during searches is reduced. The adjustment is done using the policy of 'the most candidate' for the incoming pivot selection, and the policy of 'the least discriminating' for the outgoing pivot selection.

Abstract In this paper, a new indexing and similarity search method based on dynamic selection of... more Abstract In this paper, a new indexing and similarity search method based on dynamic selection of pivots is presented. It uses Sparse Spatial Selection (SSS) for the initial selection of pivots. Two new selection policies of pivots are added, in order to the index ...

Journal of Information and …, 2011

In this paper, a new indexing and similarity search method based on dynamic selection of pivots i... more In this paper, a new indexing and similarity search method based on dynamic selection of pivots is presented. It uses Sparse Spatial Selection (SSS) for the initial selection of pivots. Two new selection policies of pivots are added, in order to the index ...

This paper presents a data structure based on Sparse Spatial Selection (SSS) for similarity searc... more This paper presents a data structure based on Sparse Spatial Selection (SSS) for similarity searching. An algorithm that tries periodically to adjust pivots to the use of database index is presented. This index is dynamic. In this way, it is possible to improve the amount of discriminations done by the pivots. So, the primary objective of indexes is achieved: to reduce the number of distance function evaluations, as it is showed in the experimentation.